Summary Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines. SnowflakeDB has been leading the charge to take advantage of cloud services that simplify the separation of compute and storage. In this episode Kent Graziano, chief technical evangelist for SnowflakeDB, explains how it is differentiated from other managed platforms and traditional data warehouse engines, the features that allow you to scale your usage dynamically, and how it allows for a shift in your workflow from ETL to ELT. If you are evaluating your options for building or migrating a data platform, then this is definitely worth a listen.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media and the Python Software Foundation. Upcoming events include the Software Architecture Conference in NYC and PyCOn US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Kent Graziano about SnowflakeDB, the cloud-native data warehouse

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what SnowflakeDB is for anyone who isn’t familiar with it?

How does it compare to the other available platforms for data warehousing? How does it differ from traditional data warehouses?

How does the performance and flexibility affect the data modeling requirements?

Snowflake is one of the data stores that is enabling the shift from an ETL to an ELT workflow. What are the features that allow for that approach and what are some of the challenges that it introduces? Can you describe how the platform is architected and some of the ways that it has evolved as it has grown in popularity?

What are some of the current limitations that you are struggling with?

For someone getting started with Snowflake what is involved with loading data into the platform?

What is their workflow for allocating and scaling compute capacity and running anlyses?

One of the interesting features enabled by your architecture is data sharing. What are some of the most interesting or unexpected uses of that capability that you have seen? What are some other features or use cases for Snowflake that are not as well known or publicized which you think users should know about? When is SnowflakeDB the wrong choice? What are some of the plans for the future of SnowflakeDB?

Contact Info

LinkedIn Website @KentGraziano on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

SnowflakeDB

Free Trial Stack Overflow

Data Warehouse Oracle DB MPP == Massively Parallel Processing Shared Nothing Architecture Multi-Cluster Shared Data Architecture Google BigQuery AWS Redshift AWS Redshift Spectrum Presto

Podcast Episode

SnowflakeDB Semi-Structured Data Types Hive ACID == Atomicity, Consistency, Isolation, Durability 3rd Normal Form Data Vault Modeling Dimensional Modeling JSON AVRO Parquet SnowflakeDB Virtual Warehouses CRM == Customer Relationship Management Master Data Management

Podcast Episode

FoundationDB

Podcast Episode

Apache Spark

Podcast Episode

SSIS == SQL Server Integration Services Talend Informatica Fivetran

Podcast Episode

Matillion Apache Kafka Snowpipe Snowflake Data Exchange OLTP == Online Transaction Processing GeoJSON Snowflake Documentation SnowAlert Splunk Data Catalog

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Summary Anomaly detection is a capability that is useful in a variety of problem domains, including finance, internet of things, and systems monitoring. Scaling the volume of events that can be processed in real-time can be challenging, so Paul Brebner from Instaclustr set out to see how far he could push Kafka and Cassandra for this use case. In this interview he explains the system design that he tested, his findings for how these tools were able to work together, and how they behaved at different orders of scale. It was an interesting conversation about how he stress tested the Instaclustr managed service for benchmarking an application that has real-world utility.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Integrating data across the enterprise has been around for decades – so have the techniques to do it. But, a new way of integrating data and improving streams has evolved. By integrating each silo independently – data is able to integrate without any direct relation. At CluedIn they call it “eventual connectivity”. If you want to learn more on how to deliver fast access to your data across the enterprise leveraging this new method, and the technologies that make it possible, get a demo or presentation of the CluedIn Data Hub by visiting dataengineeringpodcast.com/cluedin. And don’t forget to thank them for supporting the show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Paul Brebner about his experience designing and building a scalable, real-time anomaly detection system using Kafka and Cassandra

Interview

Introduction How did you get involved in the area of data management? Can you start by describing the problem that you were trying to solve and the requirements that you were aiming for?

What are some example cases where anomaly detection is useful or necessary?

Once you had established the requirements in terms of functionality and data volume, what was your approach for dete

Summary

The past year has been an active one for the timeseries market. New products have been launched, more businesses have moved to streaming analytics, and the team at Timescale has been keeping busy. In this episode the TimescaleDB CEO Ajay Kulkarni and CTO Michael Freedman stop by to talk about their 1.0 release, how the use cases for timeseries data have proliferated, and how they are continuing to simplify the task of processing your time oriented events.

Introduction

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m welcoming Ajay Kulkarni and Mike Freedman back to talk about how TimescaleDB has grown and changed over the past year

Interview

Introduction How did you get involved in the area of data management? Can you refresh our memory about what TimescaleDB is? How has the market for timeseries databases changed since we last spoke? What has changed in the focus and features of the TimescaleDB project and company? Toward the end of 2018 you launched the 1.0 release of Timescale. What were your criteria for establishing that milestone?

What were the most challenging aspects of reaching that goal?

In terms of timeseries workloads, what are some of the factors that differ across varying use cases?

How do those differences impact the ways in which Timescale is used by the end user, and built by your team?

What are some of the initial assumptions that you made while first launching Timescale that have held true, and which have been disproven? How have the improvements and new features in the recent releases of PostgreSQL impacted the Timescale product?

Have you been able to leverage some of the native improvements to simplify your implementation? Are there any use cases for Timescale that would have been previously impractical in vanilla Postgres that would now be reasonable without the help of Timescale?

What is in store for the future of the Timescale product and organization?

Contact Info

Ajay

@acoustik on Twitter LinkedIn

Mike

LinkedIn Website @michaelfreedman on Twitter

Timescale

Website Documentation Careers timescaledb on GitHub @timescaledb on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

TimescaleDB Original Appearance on the Data Engineering Podcast 1.0 Release Blog Post PostgreSQL

Podcast Interview

RDS DB-Engines MongoDB IOT (Internet Of Things) AWS Timestream Kafka Pulsar

Podcast Episode

Spark

Podcast Episode

Flink

Podcast Episode

Hadoop DevOps PipelineDB

Podcast Interview

Grafana Tableau Prometheus OLTP (Online Transaction Processing) Oracle DB Data Lake

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

As more companies and organizations are working to gain a real-time view of their business, they are increasingly turning to stream processing technologies to fullfill that need. However, the storage requirements for continuous, unbounded streams of data are markedly different than that of batch oriented workloads. To address this shortcoming the team at Dell EMC has created the open source Pravega project. In this episode Tom Kaitchuk explains how Pravega simplifies storage and processing of data streams, how it integrates with processing engines such as Flink, and the unique capabilities that it provides in the area of exactly once processing and transactions. And if you listen at approximately the half-way mark, you can hear as the hosts mind is blown by the possibilities of treating everything, including schema information, as a stream.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Tom Kaitchuck about Pravega, an open source data storage platform optimized for persistent streams

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Pravega is and the story behind it? What are the use cases for Pravega and how does it fit into the data ecosystem?

How does it compare with systems such as Kafka and Pulsar for ingesting and persisting unbounded data?

How do you represent a stream on-disk?

What are the benefits of using this format for persisted streams?

One of the compelling aspects of Pravega is the automatic sharding and resource allocation for variations in data patterns. Can you describe how that operates and the benefits that it provides? I am also intrigued by the automatic tiering of the persisted storage. How does that work and what options exist for managing the lifecycle of the data in the cluster? For someone who wants to build an application on top of Pravega, what interfaces does it provide and what architectural patterns does it lend itself toward? What are some of the unique system design patterns that are made possible by Pravega? How is Pravega architected internally? What is involved in integrating engines such as Spark, Flink, or Storm with Pravega? A common challenge for streaming systems is exactly once semantics. How does Pravega approach that problem?

Does it have any special capabilities for simplifying processing of out-of-order events?

For someone planning a deployment of Pravega, what is involved in building and scaling a cluster?

What are some of the operational edge cases that users should be aware of?

What are some of the most interesting, useful, or challenging experiences that you have had while building Pravega? What are some cases where you would recommend against using Pravega? What is in store for the future of Pravega?

Contact Info

tkaitchuk on GitHub LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooli

Summary

Processing high velocity time-series data in real-time is a complex challenge. The team at PipelineDB has built a continuous query engine that simplifies the task of computing aggregates across incoming streams of events. In this episode Derek Nelson and Usman Masood explain how it is architected, strategies for designing your data flows, how to scale it up and out, and edge cases to be aware of.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Usman Masood and Derek Nelson about PipelineDB, an open source continuous query engine for PostgreSQL

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what PipelineDB is and the motivation for creating it?

What are the major use cases that it enables? What are some example applications that are uniquely well suited to the capabilities of PipelineDB?

What are the major concepts and components that users of PipelineDB should be familiar with? Given the fact that it is a plugin for PostgreSQL, what level of compatibility exists between PipelineDB and other plugins such as Timescale and Citus? What are some of the common patterns for populating data streams? What are the options for scaling PipelineDB systems, both vertically and horizontally?

How much elasticity does the system support in terms of changing volumes of inbound data? What are some of the limitations or edge cases that users should be aware of?

Given that inbound data is not persisted to disk, how do you guard against data loss?

Is it possible to archive the data in a stream, unaltered, to a separate destination table or other storage location? Can a separate table be used as an input stream?

Since the data being processed by the continuous queries is potentially unbounded, how do you approach checkpointing or windowing the data in the continuous views? What are some of the features that you have found to be the most useful which users might initially overlook? What would be involved in generating an alert or notification on an aggregate output that was in some way anomalous? What are some of the most challenging aspects of building continuous aggregates on unbounded data? What have you found to be some of the most interesting, complex, or challenging aspects of building and maintaining PipelineDB? What are some of the most interesting or unexpected ways that you have seen PipelineDB used? When is PipelineDB the wrong choice? What do you have planned for the future of PipelineDB now that you have hit the 1.0 milestone?

Contact Info

Derek

derekjn on GitHub LinkedIn

Usman

@usmanm on Twitter Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

PipelineDB Stride PostgreSQL

Podcast Episode

AdRoll Probabilistic Data Structures TimescaleDB

[Podcast Episode](

Hive Redshift Kafka Kinesis ZeroMQ Nanomsg HyperLogLog Bloom Filter

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineerin

Summary

Apache Spark is a popular and widely used tool for a variety of data oriented projects. With the large array of capabilities, and the complexity of the underlying system, it can be difficult to understand how to get started using it. Jean George Perrin has been so impressed by the versatility of Spark that he is writing a book for data engineers to hit the ground running. In this episode he helps to make sense of what Spark is, how it works, and the various ways that you can use it. He also discusses what you need to know to get it deployed and keep it running in a production environment and how it fits into the overall data ecosystem.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Jean Georges Perrin, author of the upcoming Manning book Spark In Action 2nd Edition, about the ways that Spark is used and how it fits into the data landscape

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Spark is?

What are some of the main use cases for Spark? What are some of the problems that Spark is uniquely suited to address? Who uses Spark?

What are the tools offered to Spark users? How does it compare to some of the other streaming frameworks such as Flink, Kafka, or Storm? For someone building on top of Spark what are the main software design paradigms?

How does the design of an application change as you go from a local development environment to a production cluster?

Once your application is written, what is involved in deploying it to a production environment? What are some of the most useful strategies that you have seen for improving the efficiency and performance of a processing pipeline? What are some of the edge cases and architectural considerations that engineers should be considering as they begin to scale their deployments? What are some of the common ways that Spark is deployed, in terms of the cluster topology and the supporting technologies? What are the limitations of the Spark programming model?

What are the cases where Spark is the wrong choice?

What was your motivation for writing a book about Spark?

Who is the target audience?

What have been some of the most interesting or useful lessons that you have learned in the process of writing a book about Spark? What advice do you have for anyone who is considering or currently using Spark?

Contact Info

@jgperrin on Twitter Blog

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Book Discount

Use the code poddataeng18 to get 40% off of all of Manning’s products at manning.com

Links

Apache Spark Spark In Action Book code examples in GitHub Informix International Informix Users Group MySQL Microsoft SQL Server ETL (Extract, Transform, Load) Spark SQL and Spark In Action‘s chapter 11 Spark ML and Spark In Action‘s chapter 18 Spark Streaming (structured) and Spark In Action‘s chapter 10 Spark GraphX Hadoop Jupyter

Podcast Interview

Zeppelin Databricks IBM Watson Studio Kafka Flink

P

Summary Distributed systems are complex to build and operate, and there are certain primitives that are common to a majority of them. Rather then re-implement the same capabilities every time, many projects build on top of Apache Zookeeper. In this episode Patrick Hunt explains how the Apache Zookeeper project was started, how it functions, and how it is used as a building block for other distributed systems. He also explains the operational considerations for running your own cluster, how it compares to more recent entrants such as Consul and EtcD, and what is in store for the future.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Patrick Hunt about Apache Zookeeper and how it is used as a building block for distributed systems

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Zookeeper is and how the project got started?

What are the main motivations for using a centralized coordination service for distributed systems?

What are the distributed systems primitives that are built into Zookeeper?

What are some of the higher-order capabilities that Zookeeper provides to users who are building distributed systems on top of Zookeeper? What are some of the types of system level features that application developers will need which aren’t provided by Zookeeper?

Can you discuss how Zookeeper is architected and how that design has evolved over time?

What have you found to be some of the most complicated or difficult aspects of building and maintaining Zookeeper?

What are the scaling factors for Zookeeper?

What are the edge cases that users should be aware of? Where does it fall on the axes of the CAP theorem?

What are the main failure modes for Zookeeper?

How much of the recovery logic is left up to the end user of the Zookeeper cluster?

Since there are a number of projects that rely on Zookeeper, many of which are likely to be run in the same environment (e.g. Kafka and Flink), what would be involved in sharing a single Zookeeper cluster among those multiple services? In recent years we have seen projects such as EtcD which is used by Kubernetes, and Consul. How does Zookeeper compare with those projects?

What are some of the cases where Zookeeper is the wrong choice?

How have the needs of distributed systems engineers changed since you first began working on Zookeeper? If you were to start the project over today, what would you do differently?

Would you still use Java?

What are some of the most interesting or unexpected ways that you have seen Zookeeper used? What do you have planned for the future of Zookeeper?

Contact Info

@phunt on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Zookeeper Cloudera Google Chubby Sourceforge HBase High Availability Fallacies of distributed computing Falsehoods programmers believe about networking Consul EtcD Apache Curator Raft Consensus Algorithm Zookeeper Atomic Broadcast SSD Write Cliff Apache Kafka Apache Flink

Podcast

Summary

Modern applications and data platforms aspire to process events and data in real time at scale and with low latency. Apache Flink is a true stream processing engine with an impressive set of capabilities for stateful computation at scale. In this episode Fabian Hueske, one of the original authors, explains how Flink is architected, how it is being used to power some of the world’s largest businesses, where it sits in the lanscape of stream processing tools, and how you can start using it today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Fabian Hueske, co-author of the upcoming O’Reilly book Stream Processing With Apache Flink, about his work on Apache Flink, the stateful streaming engine

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Flink is and how the project got started? What are some of the primary ways that Flink is used? How does Flink compare to other streaming engines such as Spark, Kafka, Pulsar, and Storm?

What are some use cases that Flink is uniquely qualified to handle?

Where does Flink fit into the current data landscape? How is Flink architected?

How has that architecture evolved? Are there any aspects of the current design that you would do differently if you started over today?

How does scaling work in a Flink deployment?

What are the scaling limits? What are some of the failure modes that users should be aware of?

How is the statefulness of a cluster managed?

What are the mechanisms for managing conflicts? What are the limiting factors for the volume of state that can be practically handled in a cluster and for a given purpose? Can state be shared across processes or tasks within a Flink cluster?

What are the comparative challenges of working with bounded vs unbounded streams of data? How do you handle out of order events in Flink, especially as the delay for a given event increases? For someone who is using Flink in their environment, what are the primary means of interacting with and developing on top of it? What are some of the most challenging or complicated aspects of building and maintaining Flink? What are some of the most interesting or unexpected ways that you have seen Flink used? What are some of the improvements or new features that are planned for the future of Flink? What are some features or use cases that you are explicitly not planning to support? For people who participate in the training sessions that you offer through Data Artisans, what are some of the concepts that they are challenged by?

What do they find most interesting or exciting?

Contact Info

LinkedIn @fhueske on Twitter fhueske on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Flink Data Artisans IBM DB2 Technische Universität Berlin Hadoop Relational Database Google Cloud Dataflow Spark Cascading Java RocksDB Flink Checkpoints Flink Savepoints Kafka Pulsar Storm Scala LINQ (Language INtegrated Query) SQL Backpressure

Summary

A data lake can be a highly valuable resource, as long as it is well built and well managed. Unfortunately, that can be a complex and time-consuming effort, requiring specialized knowledge and diverting resources from your primary business. In this episode Yoni Iny, CTO of Upsolver, discusses the various components that are necessary for a successful data lake project, how the Upsolver platform is architected, and how modern data lakes can benefit your organization.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Yoni Iny about Upsolver, a data lake platform that lets developers integrate and analyze streaming data with ease

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Upsolver is and how it got started?

What are your goals for the platform?

There are a lot of opinions on both sides of the data lake argument. When is it the right choice for a data platform?

What are the shortcomings of a data lake architecture?

How is Upsolver architected?

How has that architecture changed over time? How do you manage schema validation for incoming data? What would you do differently if you were to start over today?

What are the biggest challenges at each of the major stages of the data lake? What is the workflow for a user of Upsolver and how does it compare to a self-managed data lake? When is Upsolver the wrong choice for an organization considering implementation of a data platform? Is there a particular scale or level of data maturity for an organization at which they would be better served by moving management of their data lake in house? What features or improvements do you have planned for the future of Upsolver?

Contact Info

Yoni

yoniiny on GitHub LinkedIn

Upsolver

Website @upsolver on Twitter LinkedIn Facebook

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Upsolver Data Lake Israeli Army Data Warehouse Data Engineering Podcast Episode About Data Curation Three Vs Kafka Spark Presto Drill Spot Instances Object Storage Cassandra Redis Latency Avro Parquet ORC Data Engineering Podcast Episode About Data Serialization Formats SSTables Run Length Encoding CSV (Comma Separated Values) Protocol Buffers Kinesis ETL DevOps Prometheus Cloudwatch DataDog InfluxDB SQL Pandas Confluent KSQL

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

Every business with a website needs some way to keep track of how much traffic they are getting, where it is coming from, and which actions are being taken. The default in most cases is Google Analytics, but this can be limiting when you wish to perform detailed analysis of the captured data. To address this problem, Alex Dean co-founded Snowplow Analytics to build an open source platform that gives you total control of your website traffic data. In this episode he explains how the project and company got started, how the platform is architected, and how you can start using it today to get a clearer view of how your customers are interacting with your web and mobile applications.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat This is your host Tobias Macey and today I’m interviewing Alexander Dean about Snowplow Analytics

Interview

Introductions How did you get involved in the area of data engineering and data management? What is Snowplow Analytics and what problem were you trying to solve when you started the company? What is unique about customer event data from an ingestion and processing perspective? Challenges with properly matching up data between sources Data collection is one of the more difficult aspects of an analytics pipeline because of the potential for inconsistency or incorrect information. How is the collection portion of the Snowplow stack designed and how do you validate the correctness of the data?

Cleanliness/accuracy

What kinds of metrics should be tracked in an ingestion pipeline and how do you monitor them to ensure that everything is operating properly? Can you describe the overall architecture of the ingest pipeline that Snowplow provides?

How has that architecture evolved from when you first started? What would you do differently if you were to start over today?

Ensuring appropriate use of enrichment sources What have been some of the biggest challenges encountered while building and evolving Snowplow? What are some of the most interesting uses of your platform that you are aware of?

Keep In Touch

Alex

@alexcrdean on Twitter LinkedIn

Snowplow

@snowplowdata on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Snowplow

GitHub

Deloitte Consulting OpenX Hadoop AWS EMR (Elastic Map-Reduce) Business Intelligence Data Warehousing Google Analytics CRM (Customer Relationship Management) S3 GDPR (General Data Protection Regulation) Kinesis Kafka Google Cloud Pub-Sub JSON-Schema Iglu IAB Bots And Spiders List Heap Analytics

Podcast Interview

Redshift SnowflakeDB Snowplow Insights Googl

Summary

With the attention being paid to the systems that power large volumes of high velocity data it is easy to forget about the value of data collection at human scales. Ona is a company that is building technologies to support mobile data collection, analysis of the aggregated information, and user-friendly presentations. In this episode CTO Peter Lubell-Doughtie describes the architecture of the platform, the types of environments and use cases where it is being employed, and the value of small data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Peter Lubell-Doughtie about using Ona for collecting data and processing it with Canopy

Interview

Introduction How did you get involved in the area of data management? What is Ona and how did the company get started?

What are some examples of the types of customers that you work with?

What types of data do you support in your collection platform? What are some of the mechanisms that you use to ensure the accuracy of the data that is being collected by users? Does your mobile collection platform allow for anyone to submit data without having to be associated with a given account or organization? What are some of the integration challenges that are unique to the types of data that get collected by mobile field workers? Can you describe the flow of the data from collection through to analysis? To help improve the utility of the data being collected you have started building Canopy. What was the tipping point where it became worth the time and effort to start that project?

What are the architectural considerations that you factored in when designing it? What have you found to be the most challenging or unexpected aspects of building an enterprise data warehouse for general users?

What are your plans for the future of Ona and Canopy?

Contact Info

Email pld on Github Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

OpenSRP Ona Canopy Open Data Kit Earth Institute at Columbia University Sustainable Engineering Lab WHO Bill and Melinda Gates Foundation XLSForms PostGIS Kafka Druid Superset Postgres Ansible Docker Terraform

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Summary

Data integration and routing is a constantly evolving problem and one that is fraught with edge cases and complicated requirements. The Apache NiFi project models this problem as a collection of data flows that are created through a self-service graphical interface. This framework provides a flexible platform for building a wide variety of integrations that can be managed and scaled easily to fit your particular needs. In this episode project members Kevin Doran and Andy LoPresto discuss the ways that NiFi can be used, how to start using it in your environment, and plans for future development. They also explained how it fits in the broad landscape of data tools, the interesting and challenging aspects of the project, and how to build new extensions.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kevin Doran and Andy LoPresto about Apache NiFi

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what NiFi is? What is the motivation for building a GUI as the primary interface for the tool when the current trend is to represent everything as code? How did you get involved with the project?

Where does it sit in the broader landscape of data tools?

Does the data that is processed by NiFi flow through the servers that it is running on (á la Spark/Flink/Kafka), or does it orchestrate actions on other systems (á la Airflow/Oozie)?

How do you manage versioning and backup of data flows, as well as promoting them between environments?

One of the advertised features is tracking provenance for data flows that are managed by NiFi. How is that data collected and managed?

What types of reporting are available across this information?

What are some of the use cases or requirements that lend themselves well to being solved by NiFi?

When is NiFi the wrong choice?

What is involved in deploying and scaling a NiFi installation?

What are some of the system/network parameters that should be considered? What are the scaling limitations?

What have you found to be some of the most interesting, unexpected, and/or challenging aspects of building and maintaining the NiFi project and community? What do you have planned for the future of NiFi?

Contact Info

Kevin Doran

@kevdoran on Twitter Email

Andy LoPresto

@yolopey on Twitter Email

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

NiFi HortonWorks DataFlow HortonWorks Apache Software Foundation Apple CSV XML JSON Perl Python Internet Scale Asset Management Documentum DataFlow NSA (National Security Agency) 24 (TV Show) Technology Transfer Program Agile Software Development Waterfall Spark Flink Kafka Oozie Luigi Airflow FluentD ETL (Extract, Transform, and Load) ESB (Enterprise Service Bus) MiNiFi Java C++ Provenance Kubernetes Apache Atlas Data Governance Kibana K-Nearest Neighbors DevOps DSL (Domain Specific Language) NiFi Registry Artifact Repository Nexus NiFi CLI Maven Archetype IoT Docker Backpressure NiFi Wiki TLS (Transport Layer Security) Mozilla TLS Observatory NiFi Flow Design System Data Lineage GDPR (General Data Protection Regulation)

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Summary

Building an ETL pipeline is a common need across businesses and industries. It’s easy to get one started but difficult to manage as new requirements are added and greater scalability becomes necessary. Rather than duplicating the efforts of other engineers it might be best to use a hosted service to handle the plumbing so that you can focus on the parts that actually matter for your business. In this episode CTO and co-founder of Alooma, Yair Weinberger, explains how the platform addresses the common needs of data collection, manipulation, and storage while allowing for flexible processing. He describes the motivation for starting the company, how their infrastructure is architected, and the challenges of supporting multi-tenancy and a wide variety of integrations.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Yair Weinberger about Alooma, a company providing data pipelines as a service

Interview

Introduction How did you get involved in the area of data management? What is Alooma and what is the origin story? How is the Alooma platform architected?

I want to go into stream VS batch here What are the most challenging components to scale?

How do you manage the underlying infrastructure to support your SLA of 5 nines? What are some of the complexities introduced by processing data from multiple customers with various compliance requirements?

How do you sandbox user’s processing code to avoid security exploits?

What are some of the potential pitfalls for automatic schema management in the target database? Given the large number of integrations, how do you maintain the

What are some challenges when creating integrations, isn’t it simply conforming with an external API?

For someone getting started with Alooma what does the workflow look like? What are some of the most challenging aspects of building and maintaining Alooma? What are your plans for the future of Alooma?

Contact Info

LinkedIn @yairwein on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Alooma Convert Media Data Integration ESB (Enterprise Service Bus) Tibco Mulesoft ETL (Extract, Transform, Load) Informatica Microsoft SSIS OLAP Cube S3 Azure Cloud Storage Snowflake DB Redshift BigQuery Salesforce Hubspot Zendesk Spark The Log: What every software engineer should know about real-time data’s unifying abstraction by Jay Kreps RDBMS (Relational Database Management System) SaaS (Software as a Service) Change Data Capture Kafka Storm Google Cloud PubSub Amazon Kinesis Alooma Code Engine Zookeeper Idempotence Kafka Streams Kubernetes SOC2 Jython Docker Python Javascript Ruby Scala PII (Personally Identifiable Information) GDPR (General Data Protection Regulation) Amazon EMR (Elastic Map Reduce) Sequoia Capital Lightspeed Investors Redis Aerospike Cassandra MongoDB

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Summary

Most businesses end up with data in a myriad of places with varying levels of structure. This makes it difficult to gain insights from across departments, projects, or people. Presto is a distributed SQL engine that allows you to tie all of your information together without having to first aggregate it all into a data warehouse. Kamil Bajda-Pawlikowski co-founded Starburst Data to provide support and tooling for Presto, as well as contributing advanced features back to the project. In this episode he describes how Presto is architected, how you can use it for your analytics, and the work that he is doing at Starburst Data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kamil Bajda-Pawlikowski about Presto and his experiences with supporting it at Starburst Data

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Presto is?

What are some of the common use cases and deployment patterns for Presto?

How does Presto compare to Drill or Impala? What is it about Presto that led you to building a business around it? What are some of the most challenging aspects of running and scaling Presto? For someone who is using the Presto SQL interface, what are some of the considerations that they should keep in mind to avoid writing poorly performing queries?

How does Presto represent data for translating between its SQL dialect and the API of the data stores that it interfaces with?

What are some cases in which Presto is not the right solution? What types of support have you found to be the most commonly requested? What are some of the types of tooling or improvements that you have made to Presto in your distribution?

What are some of the notable changes that your team has contributed upstream to Presto?

Contact Info

Website E-mail Twitter – @starburstdata Twitter – @prestodb

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Starburst Data Presto Hadapt Hadoop Hive Teradata PrestoCare Cost Based Optimizer ANSI SQL Spill To Disk Tempto Benchto Geospatial Functions Cassandra Accumulo Kafka Redis PostGreSQL

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Summary

Cloud computing and ubiquitous virtualization have changed the ways that our applications are built and deployed. This new environment requires a new way of tracking and addressing the security of our systems. ThreatStack is a platform that collects all of the data that your servers generate and monitors for unexpected anomalies in behavior that would indicate a breach and notifies you in near-realtime. In this episode ThreatStack’s director of operations, Pete Cheslock, and senior infrastructure security engineer, Patrick Cable, discuss the data infrastructure that supports their platform, how they capture and process the data from client systems, and how that information can be used to keep your systems safe from attackers.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Pete Cheslock and Pat Cable about the data infrastructure and security controls at ThreatStack

Interview

Introduction How did you get involved in the area of data management? Why don’t you start by explaining what ThreatStack does?

What was lacking in the existing options (services and self-hosted/open source) that ThreatStack solves for?

Can you describe the type(s) of data that you collect and how it is structured? What is the high level data infrastructure that you use for ingesting, storing, and analyzing your customer data?

How do you ensure a consistent format of the information that you receive? How do you ensure that the various pieces of your platform are deployed using the proper configurations and operating as intended? How much configuration do you provide to the end user in terms of the captured data, such as sampling rate or additional context?

I understand that your original architecture used RabbitMQ as your ingest mechanism, which you then migrated to Kafka. What was your initial motivation for that change?

How much of a benefit has that been in terms of overall complexity and cost (both time and infrastructure)?

How do you ensure the security and provenance of the data that you collect as it traverses your infrastructure? What are some of the most common vulnerabilities that you detect in your client’s infrastructure? For someone who wants to start using ThreatStack, what does the setup process look like? What have you found to be the most challenging aspects of building and managing the data processes in your environment? What are some of the projects that you have planned to improve the capacity or capabilities of your infrastructure?

Contact Info

Pete Cheslock

@petecheslock on Twitter Website petecheslock on GitHub

Patrick Cable

@patcable on Twitter Website patcable on GitHub

ThreatStack

Website @threatstack on Twitter threatstack on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

ThreatStack SecDevO

Summary

As communications between machines become more commonplace the need to store the generated data in a time-oriented manner increases. The market for timeseries data stores has many contenders, but they are not all built to solve the same problems or to scale in the same manner. In this episode the founders of TimescaleDB, Ajay Kulkarni and Mike Freedman, discuss how Timescale was started, the problems that it solves, and how it works under the covers. They also explain how you can start using it in your infrastructure and their plans for the future.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Ajay Kulkarni and Mike Freedman about Timescale DB, a scalable timeseries database built on top of PostGreSQL

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Timescale is and how the project got started? The landscape of time series databases is extensive and oftentimes difficult to navigate. How do you view your position in that market and what makes Timescale stand out from the other options? In your blog post that explains the design decisions for how Timescale is implemented you call out the fact that the inserted data is largely append only which simplifies the index management. How does Timescale handle out of order timestamps, such as from infrequently connected sensors or mobile devices? How is Timescale implemented and how has the internal architecture evolved since you first started working on it?

What impact has the 10.0 release of PostGreSQL had on the design of the project? Is timescale compatible with systems such as Amazon RDS or Google Cloud SQL?

For someone who wants to start using Timescale what is involved in deploying and maintaining it? What are the axes for scaling Timescale and what are the points where that scalability breaks down?

Are you aware of anyone who has deployed it on top of Citus for scaling horizontally across instances?

What has been the most challenging aspect of building and marketing Timescale? When is Timescale the wrong tool to use for time series data? One of the use cases that you call out on your website is for systems metrics and monitoring. How does Timescale fit into that ecosystem and can it be used along with tools such as Graphite or Prometheus? What are some of the most interesting uses of Timescale that you have seen? Which came first, Timescale the business or Timescale the database, and what is your strategy for ensuring that the open source project and the company around it both maintain their health? What features or improvements do you have planned for future releases of Timescale?

Contact Info

Ajay

LinkedIn @acoustik on Twitter Timescale Blog

Mike

Website LinkedIn @michaelfreedman on Twitter Timescale Blog

Timescale

Website @timescaledb on Twitter GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Timescale PostGreSQL Citus Timescale Design Blog Post MIT NYU Stanford SDN Princeton Machine Data Timeseries Data List of Timeseries Databases NoSQL Online Transaction Processing (OLTP) Object Relational Mapper (ORM) Grafana Tableau Kafka When Boring Is Awesome PostGreSQL RDS Google Cloud SQL Azure DB Docker Continuous Aggregates Streaming Replication PGPool II Kubernetes Docker Swarm Citus Data

Website Data Engineering Podcast Interview

Database Indexing B-Tree Index GIN Index GIST Index STE Energy Redis Graphite Prometheus pg_prometheus OpenMetrics Standard Proposal Timescale Parallel Copy Hadoop PostGIS KDB+ DevOps Internet of Things MongoDB Elastic DataBricks Apache Spark Confluent New Enterprise Associates MapD Benchmark Ventures Hortonworks 2σ Ventures CockroachDB Cloudflare EMC Timescale Blog: Why SQL is beating NoSQL, and what this means for the future of data

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Summary

One of the critical components for modern data infrastructure is a scalable and reliable messaging system. Publish-subscribe systems have been popular for many years, and recently stream oriented systems such as Kafka have been rising in prominence. This week Rajan Dhabalia and Matteo Merli discuss the work they have done on Pulsar, which supports both options, in addition to being globally scalable and fast. They explain how Pulsar is architected, how to scale it, and how it fits into your existing infrastructure.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers A few announcements:

There is still time to register for the O’Reilly Strata Conference in San Jose, CA March 5th-8th. Use the link dataengineeringpodcast.com/strata-san-jose to register and save 20% The O’Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20% If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register.

Your host is Tobias Macey and today I’m interviewing Rajan Dhabalia and Matteo Merli about Pulsar, a distributed open source pub-sub messaging system

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Pulsar is and what the original inspiration for the project was? What have been some of the most challenging aspects of building and promoting Pulsar? For someone who wants to run Pulsar, what are the infrastructure and network requirements that they should be considering and what is involved in deploying the various components? What are the scaling factors for Pulsar and what aspects of deployment and administration should users pay special attention to? What projects or services do you consider to be competitors to Pulsar and what makes it stand out in comparison? The documentation mentions that there is an API layer that provides drop-in compatibility with Kafka. Does that extend to also supporting some of the plugins that have developed on top of Kafka? One of the popular aspects of Kafka is the persistence of the message log, so I’m curious how Pulsar manages long-term storage and reprocessing of messages that have already been acknowledged? When is Pulsar the wrong tool to use? What are some of the improvements or new features that you have planned for the future of Pulsar?

Contact Info

Matteo

merlimat on GitHub @merlimat on Twitter

Rajan

@dhabaliaraj on Twitter rhabalia on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Pulsar Publish-Subscribe Yahoo Streamlio ActiveMQ Kafka Bookkeeper SLA (Service Level Agreement) Write-Ahead Log Ansible Zookeeper Pulsar Deployme

Summary

Data oriented applications that need to operate on large, fast-moving sterams of information can be difficult to build and scale due to the need to manage their state. In this episode Sean T. Allen, VP of engineering for Wallaroo Labs, explains how Wallaroo was designed and built to reduce the cognitive overhead of building this style of project. He explains the motivation for building Wallaroo, how it is implemented, and how you can start using it today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Sean T. Allen about Wallaroo, a framework for building and operating stateful data applications at scale

Interview

Introduction How did you get involved in the area of data engineering? What is Wallaroo and how did the project get started? What is the Pony language, and what features does it have that make it well suited for the problem area that you are focusing on? Why did you choose to focus first on Python as the language for interacting with Wallaroo and how is that integration implemented? How is Wallaroo architected internally to allow for distributed state management?

Is the state persistent, or is it only maintained long enough to complete the desired computation? If so, what format do you use for long term storage of the data?

What have been the most challenging aspects of building the Wallaroo platform? Which axes of the CAP theorem have you optimized for? For someone who wants to build an application on top of Wallaroo, what is involved in getting started? Once you have a working application, what resources are necessary for deploying to production and what are the scaling factors?

What are the failure modes that users of Wallaroo need to account for in their application or infrastructure?

What are some situations or problem types for which Wallaroo would be the wrong choice? What are some of the most interesting or unexpected uses of Wallaroo that you have seen? What do you have planned for the future of Wallaroo?

Contact Info

IRC Mailing List Wallaroo Labs Twitter Email Personal Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Wallaroo Labs Storm Applied Apache Storm Risk Analysis Pony Language Erlang Akka Tail Latency High Performance Computing Python Apache Software Foundation Beyond Distributed Transactions: An Apostate’s View Consistent Hashing Jepsen Lineage Driven Fault Injection Chaos Engineering QCon 2016 Talk Codemesh in London: How did I get here? CAP Theorem CRDT Sync Free Project Basho Wallaroo on GitHub Docker Puppet Chef Ansible SaltStack Kafka TCP Dask Data Engineering Episode About Dask Beowulf Cluster Redis Flink Haskell

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Summary

To process your data you need to know what shape it has, which is why schemas are important. When you are processing that data in multiple systems it can be difficult to ensure that they all have an accurate representation of that schema, which is why Confluent has built a schema registry that plugs into Kafka. In this episode Ewen Cheslack-Postava explains what the schema registry is, how it can be used, and how they built it. He also discusses how it can be extended for other deployment targets and use cases, and additional features that are planned for future releases.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Ewen Cheslack-Postava about the Confluent Schema Registry

Interview

Introduction How did you get involved in the area of data engineering? What is the schema registry and what was the motivating factor for building it? If you are using Avro, what benefits does the schema registry provide over and above the capabilities of Avro’s built in schemas? How did you settle on Avro as the format to support and what would be involved in expanding that support to other serialization options? Conversely, what would be involved in using a storage backend other than Kafka? What are some of the alternative technologies available for people who aren’t using Kafka in their infrastructure? What are some of the biggest challenges that you faced while designing and building the schema registry? What is the tipping point in terms of system scale or complexity when it makes sense to invest in a shared schema registry and what are the alternatives for smaller organizations? What are some of the features or enhancements that you have in mind for future work?

Contact Info

ewencp on GitHub Website @ewencp on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Kafka Confluent Schema Registry Second Life Eve Online Yes, Virginia, You Really Do Need a Schema Registry JSON-Schema Parquet Avro Thrift Protocol Buffers Zookeeper Kafka Connect

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

Building a data pipeline that is reliable and flexible is a difficult task, especially when you have a small team. Astronomer is a platform that lets you skip straight to processing your valuable business data. Ry Walker, the CEO of Astronomer, explains how the company got started, how the platform works, and their commitment to open source.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.dataengineeringpodcast.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers This is your host Tobias Macey and today I’m interviewing Ry Walker, CEO of Astronomer, the platform for data engineering.

Interview

Introduction How did you first get involved in the area of data management? What is Astronomer and how did it get started? Regulatory challenges of processing other people’s data What does your data pipelining architecture look like? What are the most challenging aspects of building a general purpose data management environment? What are some of the most significant sources of technical debt in your platform? Can you share some of the failures that you have encountered while architecting or building your platform and company and how you overcame them? There are certain areas of the overall data engineering workflow that are well defined and have numerous tools to choose from. What are some of the unsolved problems in data management? What are some of the most interesting or unexpected uses of your platform that you are aware of?

Contact Information

Email @rywalker on Twitter

Links

Astronomer Kiss Metrics Segment Marketing tools chart Clickstream HIPAA FERPA PCI Mesos Mesos DC/OS Airflow SSIS Marathon Prometheus Grafana Terraform Kafka Spark ELK Stack React GraphQL PostGreSQL MongoDB Ceph Druid Aries Vault Adapter Pattern Docker Kinesis API Gateway Kong AWS Lambda Flink Redshift NOAA Informatica SnapLogic Meteor

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast